



import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english







@require_tokenizers
class 				_a				(   _lowerCAmelCase ,		unittest.TestCase					):
			A      		=	MobileBertTokenizer
			A      		=	MobileBertTokenizerFast
			A      		=	True
			A      		=	True
			A      		=	filter_non_english
			A      		=	'''google/mobilebert-uncased'''

			def      __snake_case       (self					)       ->   Optional[Any]:
						super().setUp()

						UpperCAmelCase_:		Union[str, Any]							      =     [
						    """[UNK]""",
						    """[CLS]""",
						    """[SEP]""",
						    """[PAD]""",
						    """[MASK]""",
						    """want""",
						    """##want""",
						    """##ed""",
						    """wa""",
						    """un""",
						    """runn""",
						    """##ing""",
						    """,""",
						    """low""",
						    """lowest""",
						]
						UpperCAmelCase_:		str							      =     os.path.join(self.tmpdirname,   VOCAB_FILES_NAMES["""vocab_file"""]					)
						with open(self.vocab_file,   """w""",   encoding="""utf-8"""					) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)

						UpperCAmelCase_:		List[str]							      =     [
						    (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
						    for tokenizer_def in self.tokenizers_list
						]

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Tuple:
						UpperCAmelCase_:		Union[str, Any]							      =     """UNwant\u00E9d,running"""
						UpperCAmelCase_:		Tuple							      =     """unwanted, running"""
						return input_text, output_text

			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		Optional[int]							      =     self.tokenizer_class(self.vocab_file					)

						UpperCAmelCase_:		str							      =     tokenizer.tokenize("""UNwant\u00E9d,running"""					)
						self.assertListEqual(SCREAMING_SNAKE_CASE_,   ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]					)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_					),   [9, 6, 7, 12, 10, 11]					)

			def      __snake_case       (self					)       ->   Optional[int]:
						if not self.test_rust_tokenizer:
									return

						UpperCAmelCase_:		Optional[int]							      =     self.get_tokenizer()
						UpperCAmelCase_:		Union[str, Any]							      =     self.get_rust_tokenizer()

						UpperCAmelCase_:		Union[str, Any]							      =     """UNwant\u00E9d,running"""

						UpperCAmelCase_:		Optional[Any]							      =     tokenizer.tokenize(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_					)
						self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Any							      =     tokenizer.encode(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     rust_tokenizer.encode(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
						self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		List[Any]							      =     self.get_rust_tokenizer()
						UpperCAmelCase_:		Tuple							      =     tokenizer.encode(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     rust_tokenizer.encode(SCREAMING_SNAKE_CASE_					)
						self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						# With lower casing
						UpperCAmelCase_:		int							      =     self.get_tokenizer(do_lower_case=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     self.get_rust_tokenizer(do_lower_case=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Tuple							      =     """UNwant\u00E9d,running"""

						UpperCAmelCase_:		Dict							      =     tokenizer.tokenize(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_					)
						self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Union[str, Any]							      =     tokenizer.encode(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     rust_tokenizer.encode(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
						self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		List[str]							      =     self.get_rust_tokenizer()
						UpperCAmelCase_:		int							      =     tokenizer.encode(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     rust_tokenizer.encode(SCREAMING_SNAKE_CASE_					)
						self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Optional[int]							      =     BasicTokenizer()

						self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""					),   ["""ah""", """\u535A""", """\u63A8""", """zz"""]					)

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		Any							      =     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_					)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """					),   ["""hello""", """!""", """how""", """are""", """you""", """?"""]					)
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""					),   ["""hello"""]					)

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Optional[Any]							      =     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_,   strip_accents=SCREAMING_SNAKE_CASE_					)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """					),   ["""hällo""", """!""", """how""", """are""", """you""", """?"""]					)
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""					),   ["""h\u00E9llo"""]					)

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		List[str]							      =     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_,   strip_accents=SCREAMING_SNAKE_CASE_					)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """					),   ["""hallo""", """!""", """how""", """are""", """you""", """?"""]					)
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""					),   ["""hello"""]					)

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Union[str, Any]							      =     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_					)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """					),   ["""hallo""", """!""", """how""", """are""", """you""", """?"""]					)
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""					),   ["""hello"""]					)

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Optional[int]							      =     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_					)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """					),   ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]					)

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		Dict							      =     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_,   strip_accents=SCREAMING_SNAKE_CASE_					)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """					),   ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]					)

			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		int							      =     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_,   strip_accents=SCREAMING_SNAKE_CASE_					)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """					),   ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]					)

			def      __snake_case       (self					)       ->   List[Any]:
						UpperCAmelCase_:		Union[str, Any]							      =     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_,   never_split=["""[UNK]"""]					)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""					),   ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]					)

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		Any							      =     ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

						UpperCAmelCase_:		Tuple							      =     {}
						for i, token in enumerate(SCREAMING_SNAKE_CASE_					):
									UpperCAmelCase_:		str							      =     i
						UpperCAmelCase_:		Union[str, Any]							      =     WordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE_,   unk_token="""[UNK]"""					)

						self.assertListEqual(tokenizer.tokenize(""""""					),   []					)

						self.assertListEqual(tokenizer.tokenize("""unwanted running"""					),   ["""un""", """##want""", """##ed""", """runn""", """##ing"""]					)

						self.assertListEqual(tokenizer.tokenize("""unwantedX running"""					),   ["""[UNK]""", """runn""", """##ing"""]					)

			def      __snake_case       (self					)       ->   List[Any]:
						self.assertTrue(_is_whitespace(""" """					)					)
						self.assertTrue(_is_whitespace("""\t"""					)					)
						self.assertTrue(_is_whitespace("""\r"""					)					)
						self.assertTrue(_is_whitespace("""\n"""					)					)
						self.assertTrue(_is_whitespace("""\u00A0"""					)					)

						self.assertFalse(_is_whitespace("""A"""					)					)
						self.assertFalse(_is_whitespace("""-"""					)					)

			def      __snake_case       (self					)       ->   str:
						self.assertTrue(_is_control("""\u0005"""					)					)

						self.assertFalse(_is_control("""A"""					)					)
						self.assertFalse(_is_control(""" """					)					)
						self.assertFalse(_is_control("""\t"""					)					)
						self.assertFalse(_is_control("""\r"""					)					)

			def      __snake_case       (self					)       ->   List[str]:
						self.assertTrue(_is_punctuation("""-"""					)					)
						self.assertTrue(_is_punctuation("""$"""					)					)
						self.assertTrue(_is_punctuation("""`"""					)					)
						self.assertTrue(_is_punctuation("""."""					)					)

						self.assertFalse(_is_punctuation("""A"""					)					)
						self.assertFalse(_is_punctuation(""" """					)					)

			def      __snake_case       (self					)       ->   List[Any]:
						UpperCAmelCase_:		Union[str, Any]							      =     self.get_tokenizer()
						UpperCAmelCase_:		Dict							      =     self.get_rust_tokenizer()

						# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
						self.assertListEqual([tokenizer.tokenize(SCREAMING_SNAKE_CASE_					) for t in ["""Test""", """\xad""", """test"""]],   [["""[UNK]"""], [], ["""[UNK]"""]]					)

						self.assertListEqual(
						    [rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_					) for t in ["""Test""", """\xad""", """test"""]],   [["""[UNK]"""], [], ["""[UNK]"""]]					)

			@slow
			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Optional[int]							      =     self.tokenizer_class.from_pretrained("""google/mobilebert-uncased"""					)

						UpperCAmelCase_:		Optional[Any]							      =     tokenizer.encode("""sequence builders""",   add_special_tokens=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     tokenizer.encode("""multi-sequence build""",   add_special_tokens=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		str							      =     tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						assert encoded_sentence == [101] + text + [102]
						assert encoded_pair == [101] + text + [102] + text_a + [102]

			def      __snake_case       (self					)       ->   Union[str, Any]:
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'					):
												UpperCAmelCase_:		List[str]							      =     self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

												UpperCAmelCase_:		Union[str, Any]							      =     f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
												UpperCAmelCase_:		int							      =     tokenizer_r.encode_plus(
												    SCREAMING_SNAKE_CASE_,   return_attention_mask=SCREAMING_SNAKE_CASE_,   return_token_type_ids=SCREAMING_SNAKE_CASE_,   return_offsets_mapping=SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_,   )

												UpperCAmelCase_:		List[str]							      =     tokenizer_r.do_lower_case if hasattr(SCREAMING_SNAKE_CASE_,   """do_lower_case"""					) else False
												UpperCAmelCase_:		List[Any]							      =     (
												    [
												        ((0, 0), tokenizer_r.cls_token),
												        ((0, 1), """A"""),
												        ((1, 2), ""","""),
												        ((3, 5), """na"""),
												        ((5, 6), """##ï"""),
												        ((6, 8), """##ve"""),
												        ((9, 15), tokenizer_r.mask_token),
												        ((16, 21), """Allen"""),
												        ((21, 23), """##NL"""),
												        ((23, 24), """##P"""),
												        ((25, 33), """sentence"""),
												        ((33, 34), """."""),
												        ((0, 0), tokenizer_r.sep_token),
												    ]
												    if not do_lower_case
												    else [
												        ((0, 0), tokenizer_r.cls_token),
												        ((0, 1), """a"""),
												        ((1, 2), ""","""),
												        ((3, 8), """naive"""),
												        ((9, 15), tokenizer_r.mask_token),
												        ((16, 21), """allen"""),
												        ((21, 23), """##nl"""),
												        ((23, 24), """##p"""),
												        ((25, 33), """sentence"""),
												        ((33, 34), """."""),
												        ((0, 0), tokenizer_r.sep_token),
												    ]
												)

												self.assertEqual(
												    [e[1] for e in expected_results],   tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]					)					)
												self.assertEqual([e[0] for e in expected_results],   tokens["""offset_mapping"""]					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Union[str, Any]							      =     ["""的""", """人""", """有"""]
						UpperCAmelCase_:		str							      =     """""".join(SCREAMING_SNAKE_CASE_					)
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'					):
												UpperCAmelCase_:		List[Any]							      =     True
												UpperCAmelCase_:		Any							      =     self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Dict							      =     self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

												UpperCAmelCase_:		Optional[Any]							      =     tokenizer_p.encode(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Optional[Any]							      =     tokenizer_r.encode(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)

												UpperCAmelCase_:		List[str]							      =     tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Optional[int]							      =     tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_					)

												# it is expected that each Chinese character is not preceded by "##"
												self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
												self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

												UpperCAmelCase_:		Any							      =     False
												UpperCAmelCase_:		int							      =     self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		str							      =     self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

												UpperCAmelCase_:		Union[str, Any]							      =     tokenizer_r.encode(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		List[str]							      =     tokenizer_p.encode(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)

												UpperCAmelCase_:		str							      =     tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Optional[int]							      =     tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_					)

												# it is expected that only the first Chinese character is not preceded by "##".
												UpperCAmelCase_:		Union[str, Any]							      =     [
												    f'##{token}' if idx != 0 else token for idx, token in enumerate(SCREAMING_SNAKE_CASE_					)
												]
												self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
												self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)











from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


a      :  Dict			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	['''pixel_values''']

			def __init__(self,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = 1 / 255,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   None:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     size if size is not None else {"""height""": 256, """width""": 256}
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						UpperCAmelCase_:		Dict							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		int							      =     do_resize
						UpperCAmelCase_:		Any							      =     size
						UpperCAmelCase_:		List[Any]							      =     resample
						UpperCAmelCase_:		int							      =     do_center_crop
						UpperCAmelCase_:		Dict							      =     crop_size
						UpperCAmelCase_:		Tuple							      =     do_rescale
						UpperCAmelCase_:		Tuple							      =     rescale_factor
						UpperCAmelCase_:		Dict							      =     do_normalize
						UpperCAmelCase_:		Dict							      =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						UpperCAmelCase_:		int							      =     image_std if image_std is not None else IMAGENET_STANDARD_STD

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Tuple							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return resize(
						    SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   resample=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return center_crop(SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   int:
						return rescale(SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						return normalize(SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,   **SCREAMING_SNAKE_CASE_,   )       ->   PIL.Image.Image:
						UpperCAmelCase_:		Any							      =     do_resize if do_resize is not None else self.do_resize
						UpperCAmelCase_:		Dict							      =     resample if resample is not None else self.resample
						UpperCAmelCase_:		Tuple							      =     do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCAmelCase_:		str							      =     do_rescale if do_rescale is not None else self.do_rescale
						UpperCAmelCase_:		List[Any]							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCAmelCase_:		List[Any]							      =     do_normalize if do_normalize is not None else self.do_normalize
						UpperCAmelCase_:		Optional[Any]							      =     image_mean if image_mean is not None else self.image_mean
						UpperCAmelCase_:		Dict							      =     image_std if image_std is not None else self.image_std

						UpperCAmelCase_:		Any							      =     size if size is not None else self.size
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     crop_size if crop_size is not None else self.crop_size
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		str							      =     make_list_of_images(SCREAMING_SNAKE_CASE_					)

						if not valid_images(SCREAMING_SNAKE_CASE_					):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

						if do_resize and size is None or resample is None:
									raise ValueError("""Size and resample must be specified if do_resize is True."""					)

						if do_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

						if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

						# All transformations expect numpy arrays.
						UpperCAmelCase_:		Tuple							      =     [to_numpy_array(SCREAMING_SNAKE_CASE_					) for image in images]

						if do_resize:
									UpperCAmelCase_:		int							      =     [self.resize(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_center_crop:
									UpperCAmelCase_:		Dict							      =     [self.center_crop(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_rescale:
									UpperCAmelCase_:		Optional[int]							      =     [self.rescale(image=SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_normalize:
									UpperCAmelCase_:		int							      =     [self.normalize(image=SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     {"""pixel_values""": images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE_,   tensor_type=SCREAMING_SNAKE_CASE_					)






